In recent years, modes of providing and using website content have been changing rapidly. At the outset, most of the contents were provided by a few professional content authors in the same manner as was done for conventional media, whereas most web users only consumed or used the created contents. However, with the advent of WEB 2.0, which is focused on user participation and user cooperation, general users began to participate in creating web contents, and the user created contents (UCC) were uploaded to web blogs or web sharing sites for consumption by other users. Therefore, the proportion of the user created content to the total web content is increasing. The UCC can be found in blogs, web forums, and other sites for sharing photos and videos. Many users are providing the contents created by themselves through the social network platform. With this explosive increase in providing UCC, it is necessary to provide technology for searching and recommending good quality contents among the huge amount of UCCs.
Conventional services, such as movie information providing services or video content providing services, provide sufficient textual information on movies or videos, such as the title, summary, actors/actresses, awards etc. Thus, the user is able to easily choose between the content based on this textual information or contents classification according to the times of creation or genres of contents can be provided with users to facilitate searching content. However, unlike the conventional content created by professionals, standardized metadata and classified contents may not be ensured to be provided in UCCs. That is, although there may be only a small difference in quality between the content provided by a limited number of suppliers such as professionals, the content quality of the UCCs may vary greatly, ranging from a very low level to the expert level. Therefore, both filtering and ranking contents are useful in providing UCCs.
In an environment where the users participate in many interactions between one another and/or between the user and contents, the user activities may provide clues for estimating the quality of the UCCs of the participating user. Therefore, it is preferable to conduct a search into information on the relationships between the users, between the users and the contents, as well as information on the contents.
Conventionally, keyword-based search algorithms, such as PageRank™, TrustRank™, Anti-Trust Rank™, and XRank™, have been used to search web page information. Specifically, the PageRank™ algorithm iterates the importance computation to the hyperlinked documents, following the links. In the PageRank™ algorithm, a document is rated as having higher importance and is thus promoted to a higher rank, if it is hyperlinked by multiple other documents. The TrustRank™ algorithm provides a method in which a set of reliable web documents is first decided, and spam web documents are removed through complex linking schemes similar to the scheme found in the PageRank™ algorithm. In the Anti-Trust Rank algorithm, a trust score is propagated in the reverse direction along the incoming links, starting from a seed set of untrustworthy spam documents. Finally, the XRank™ algorithm computes the importance and the popularity of the web documents separately and uses the computed importance and popularity for ranking purpose, in contrast to the above algorithms, which determine the importance of the web documents based on the link relation.
The above keyword-based search algorithms, however, cannot be directly applied to the UCCs because the UCCs include little textual information. That is, the documents, to which the above algorithms are applicable, should be configured by one kind of a hyperlink in the networks but the UCCs have various kinds of links depending on the user interactions in the social network. Further, the UCCs involve various data, which the web documents do not have, so that it may not be possible to obtain a high accuracy search result for the UCCs without taking into account various data. In addition, it is almost impossible to select the good quality contents among the increasing number of UCCs with insufficient number of workers who are engaged in rating the quality of UCCs. Therefore, there is a need to provide an algorithm for determining and searching the high quality UCCs based on information related to the UCCs.